Monitoring and Disrupting Dark Networks A Bias toward the Center and What It Costs Us

The goal of this article is to explore this analytic bias--how it is manifested, why it appears so extensive, and what unwitting limitations it imposes on our strategic options to counter terrorism. We use data from a study of the Syrian opposition network that was conducted in the CORE Lab at the Naval Postgraduate School in Monterey California (Lucente and Wilson 2013). The original study sought to provide a window into the armed opposition units against the regime of Syrian President Bashar Assad. This article proceeds as follows: We begin by reviewing the various strategies that can be used for disrupting dark networks. These can be broken down into two broad categories -- kinetic and non-kinetic. The former uses coercive means for disruption while the latter seeks to undermine dark networks using with subtler applications of power. Drawing on a previous analysis, we illustrate how some of these strategies can be implemented, while at the same time highlighting our own bias in that study toward central actors. We then turn to an analysis of the Syrian opposition network, highlighting how a central focus can blind analysts to other important aspects of a network; in this case, elements that ultimately aligned themselves with the Islamic State of Syria (ISIS). We conclude with some implications for the future use of SNA to monitor and disrupt dark networks

Inter-Organizational Networks and Third Sector: Emerging Features from Two Case Studies in Southern Italy

Social Network Analysis is a useful technique for studying emergent behaviours of cooperation, intervention and governance in inter-organizational networks. In this work, an empirical study of two networks of organizations operating in local territories in Southern Italy and focusing on Third Sector and welfare activities is presented. The actors are committed to experimenting a model of coordinated intervention induced by two corresponding egos which are local Caritas centres. The nodes of the two graphs are determined by combining ego- network and whole-network approaches. The weighted edges representing mutual knowledge and collaboration between nodes are determined through interviews with all actors of the local groups. It is shown that metric properties of the networks can be useful indicators to monitor and evaluate endogenous features, e.g. relational and structural embeddedness, and exogenous features characterized by homophilic mechanisms. The analysis provides insights on the networks governance of the social interacting organizations and reliable descriptors of the social processes that govern their functioning